A Unifying View of Knowledge Representation for Inductive Learning

This paper provides a foundation for inductive learning based on the use of higherorder logic for knowledge representation. In particular, the paper (i) provides a systematic individuals-as-terms approach to knowledge representation for inductive learning, and demonstrates the utility of types and higher-order constructs for this purpose; (ii) gives a systematic way of constructing predicates for use in induced definitions; (iii) widens the applicability of decision-tree algorithms beyond the usual attribute-value setting to the classification of individuals with complex structure; and (iv) shows how to induce definitions which are comprehensible and have predictive power. The paper contains ten illustrative applications involving a variety of types to which a decision-tree learning system is applied. The effectiveness of the approach is further demonstrated by applying the learning system to two larger benchmark applications.

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